Smart parking meter systems have been developed, such as those disclosed in U.S. Pat. Pub. Nos. 2014/0214499 A1 and 2014/0214500 A1 (which are both incorporated herein by reference in their entirety herein), which can monitor a given parking space for violations and automatically issue a citation electronically if a violation occurs. In order for such smart parking meters to operate automatically, they must have the ability to detect when a vehicle enters and leaves the parking space being monitored. Attempts to use in-ground sensors have been made. Alternatively, attempts have been made to use the images collected by the smart parking meter's camera(s) to determine vehicle presence. Both methods have drawbacks.
In-ground sensors typically sense magnetic field strength. For example, the sensor is embedded in the roadway above the location where a vehicle would be parked. When a vehicle is parked over the sensor, the magnetic field increases above a pre-set threshold value, which causes the meter to conclude that a vehicle is present in the monitored space.
A vehicle leaving the parking space is determined in the similar, but opposite manner. The magnetic field drops below a pre-set threshold, which is read by the meter as a change of state to indicate a vehicle leaving the monitored space.
Unfortunately, the in-ground magnetic sensors are prone to magnetic bouncing, which is where the magnetic field reading fluctuates due to a variety of factors. For example, a large industrial truck, such as a plow, driving by on the street next to the parking space will cause an increase and then a decrease in the measured magnetic field. This can cause the meter to correspondingly incorrectly determine that the parked vehicle has left the parking space and then re-entered the space. As a result, the vehicle could be issued a ticket even though it never moved. Weather can similarly affect the operation of the magnetic in ground sensors. Applicant has found that the in-ground magnetic sensors are only about 92-93% accurate.
Employing machine vision using the cameras on the meters poses problems as well. For example, machine vision at night or in rain or snow is less reliable, and sun angles reflecting off of vehicles and shining into the cameras can cause reading anomalies. The cameras can also become covered with snow or dirt, which compromises their ability to read vehicle presence.
Thus, there is a need to provide improved automated vehicle detection systems and methods.